Development of quantum informed models for the simulation of nitride semiconductor materials

III-N materials: GaN, AlN, InN and their alloys have been presented as interesting semiconductors for the development of optoelectronic devices thanks to their characteristics: their wide direct band gap, high thermal conductivity, high mechanical stability and resistance to radiation. The quality of III-N properties depends on the quality of their deposited layer, but the production of thin GaN, InN, InGaN or InAlN layers leads to the creation of structural defects such as dislocations and stacking faults. These defects are the main cause of degradation of properties in III-V materials, with the appearance of macroscopic defects or increased roughness which ultimately lead to a decrease in optical performance. This research seeks to consolidate knowledge acquired on semiconductor deposition by improving the simulation methods developed in the laboratory. Previous work has made it possible to qualitatively identify the optimal growth conditions. Nevertheless, so-called ‘classical’ atomic-scale models remain a limiting factor in moving to a quantitative scale.

Molecular simulation has benefited from recent advances in machine learning and generative artificial intelligence to such an extent that several methods for generating classical models with near-quantum precision are now reaching maturity. However, the models resulting from these methods are still specific to the problems or materials for which they were developed. Their complexity also increases with the number of different atoms involved. The objective of this work is to develop tools for developing potentials dedicated to III-V nitrides in a generic manner. The work will focus on determining which models give meaningful results by finding the right balance between accuracy, computation time and development cost, while allowing the integration of new species from GaN to InGaN or InAlN compounds. The tools developed will be applied to defects, phase segregation phenomena, or the growth process of these materials.

The CIMAP laboratory offers an environment where both experimental and theoretical investigation on nitride materials are conducted, in particular in the PM2E team. The candidate will be able to meet with state of the art experts on the topic of semiconductors with experimental and modelling expertise. Integration in the relevant international scientific communities will also be promoted through application for international congresses and promotion of the work on the national and international scales.

The funding of the position is dependant on the acceptance of the candidate by the funding agencies.

UNICAEN is a major player of and a driving force for the development of higher education and research in Normandy. It welcomes more than 32,000 students on several campuses in the Caen conurbation and in the cities of Cherbourg-en-Cotentin, Alençon, Lisieux, Saint-Lô and Vire.
At UNICAEN, you can treat yourself to all of the Normandy region’s perks, such as the coastline, its proximity to the United Kingdom, fast and easy access to Paris and cultural attractions and sporting activities.

The university is reknown for its scientific excellence which is illustrated by

• Cooperation with ENSICAEN (National School of Engineering), CYCERON (biomedical imaging platform), GANIL (National Large Heavy Ion Accelerator), CHU (University Hospital), CLCC François Baclesse (Regional Cancer Care Center)
• A strengthened partnership with the University Hospital (CHU)
• A modern unit for health research and academic training
• Local entities involved in academic training, research and culture. The evolution of staff capacity and the development of research units led to the construction of other campuses in Caen and of 6 remote sites across the region.

Applicant finishing or about to finish a master level diploma focused on or close to materials sciences, physics or theoretical chemistry.

Highly required skills:

• Experience in programming (C++, Fortran ou Python...) and a sharp interest in numerical methods and high performance computing.
• Knowledge in statistical physics, solid state physics, theoretical/quantum chemistry or atomistic modeling. Serious usage LAMMPS and Quantum Espresso software as well as computational chemistry packages (VMD, Ovito, Ase, MDA...) are especially considered.
• Materials science, especially semi-conductor related.

Some training in at least one of these fields is mandatory for your application to be considered.

Other valuable skills:

• Linux environment and HPC software (slurm)
• Synthesis skills, collaborative, autonomous and curious traits.
• Good scientific English in both reading and writing, french fluency is also a notable plus.